Data Scrubbing is also known as data cleansing. It is ultimately the process of cleaning your data, by getting rid of duplications, incorrect, and inaccurate data from your data set. In a wider sense, it involves recognising and replacing all different types of errors in your record.
Data scrubbing can help in ensuring only high-quality data remains, which can then be used in analysis. This whole process helps in improving the accuracy level of the gathered data.
Data scrubbing is a very important procedure in many sectors, especially the data intensive industries like financial services, telecommunications and so on. Why? Data is a major component of their day to day functioning.
There are many issues that commonly trigger the need for data scrubbing and cleansing. They can be in the form of human errors and can result from businesses that combine datasets from multiple sources and even legacy systems that contain old data.
The errors and issues can be a result of the below occurring:
Data redundancy and duplications can have many unpleasant consequences when performing administrative and analytical tasks. In the worst case, it will lead to real bias in the results that cause misrepresentation in the data.
As you might expect, human errors and typos are very common. Everyday entering of data can certainly result in an error of some kind. Examples can include misspellings, capitalisation errors and so on.
This can easily happen when having the same data or data records with different assigned attributes. For example, having multiple addresses for a place can lead to delivery issues.
When the data has missing values, some applications may not get processed due to incomplete records or missing attributes.
When entering data into a system, there are several ways for this data to be represented in an inconsistent way. A common example is date formats. They can be 14-06-2021 or 14/06/2021. Data scrubbing helps in maintaining a consistent format throughout the whole system for better data representation.
How can you solve these issues? Data scrubbing. Having an efficient data scrubbing tool will assist in having all data sets that’s consistent and error-free. Data scrubbing automatically recognises duplications, errors, conflicting data, incomplete and inconsistent data and ultimately eliminates them from the data set.
As you can imagine, in the past, the process of data scrubbing was done manually, which made the whole procedure prone to errors and inaccuracy, not to mention the significant cost and resource required.
Moreover, it can either make or break a business, as having poor data quality can lead to major issues that cause reoccurring problems. It’s no wonder why an array of data scrubbing tools and processes have emerged to assist in minimising the losses that can occur.
There are plenty of data scrubbing tools that different organisations use. Even though some tools do not function similarly, the steps taken to perform the data scrubbing process will usually consist of the following:
In this step, data scrubbing helps in identifying and acknowledging data that is inconsistent and irrelevant. Detecting the incorrect data first gives you clues about the overall performance of your system and helps you solve the issues that arise.
This step involves the actual process of resolving the issues mentioned in the previous section.It includes the process of de-duplications, removing irrelevancy, and repairing inconsistencies. Formatting issues are also settled in this step, as well as fixing separate individual problems.
After cleaning the data, there is another step of auditing. This is where verification occurs to test the results and ensure that all data is following the pre-set criteria and regulations.
The results in this step are transferred to a report that highlights the trends and progress of the data. This helps in bringing the data scrubbing process to the real world and allowing it to become easily understood.
In this final step, comes the process of the quality assurance where after the issues are identified, you can create your methods of modifications. From here, you can decide the next measures to be taken that will prevent the same mistakes from happening in the future.
Having clean and organised data helps in many ways, it can have very positive outcomes on your personal and professional data set. It can lead to many advantages and benefits to boost your business performance. Some of these benefits are:
Having a clean and organised data set leads to having a better decision-making process as using data scrubbing removes the errors and inaccuracies that usually disrupt decision making.
Using data scrubbing tools and techniques will save you time, and it will reduce the costs of many operations. This gives you extra time to focus on key tasks instead of spending time on onerous, labour-intensive tasks.
As a knock-on benefit, scrubbing data enables you to unlock a goldmine of value for your business. Once the data is cleaned, you can centralise your data and build applications on top of this ‘single source of truth.’ This can help you solve an array of complex problems, including removing data silos in your business.
OpenRefine (previously Google Refine) is an open source and powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.
Cost: Free (open source)
Trifacta helps businesses reduce the amount of resources spent on data scrubbing. With an easier data scrubbing process, businesses are able to analyse more data and get more out of the analysis.
Cost: Start package at $80 per user a month to $400 for its professional package.
IBM supports your data quality and information governance initiatives. It enables you to investigate, cleanse and manage your data, helping you maintain consistent views of key entities including customers, vendors, locations and products.
Cost: Book a consultation
Xplenty combines a drag-and-drop interface and personalised user support to empower any member of your organization to design sophisticated extract, transform, and load pipelines. Connect to over 140 data sources including databases, data warehouses, and cloud-based SaaS platforms
Cost: Based on connectors, get in touch
Refine your dataset by creating validation rules from the patterns you've identified, visualise trends, outliers, and patterns in your dataset and cleanse your data and remove duplicates, check addresses, and format your dataset.
Cost: $100 per month for its starter and $225 for its premium package
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